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Record W2318493644 · doi:10.1061/41082(362)68

A Method to Assess Peak Storm Wind Speeds Using Detailed Damage Surveys

2009· article· en· W2318493644 on OpenAlexaffabout
Gregory A. Kopp, Murray J. Morrison, Bahareh Kordi, Craig A. Miller

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsWestern University
Fundersnot available
KeywordsWind speedRoofStormRadarEnvironmental scienceMeteorologyGeologyWind engineeringDebrisWind tunnelStructural engineeringEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

A detailed damage survey of a single, wood-framed, structure, which had a complete roof failure during the passage of a gust front in southern Ontario, was performed. Radar data was used to estimate upper level wind speeds associated with the gust front. Details pertaining to the structural failure, including the debris field, were obtained. Wind tunnel pressure time histories, in a simulated atmospheric boundary layer, were used to establish the roof height, gust wind speed at failure. This speed was smaller than the upper level speed found from the analysis of the radar. The flight of the roof was also examined, and confirmed the wind speeds obtained from the analysis of the failure. The study illustrates that detailed damage surveys, which incorporate the use of wind tunnel test data and debris flight in the analysis, can shed considerable light on the details of the wind speeds at failure, reducing the uncertainty caused by the many assumptions in such analyses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.040
GPT teacher head0.312
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2009
Admission routes2
Has abstractyes

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